! git log -1 --format="%H"

import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 0
# The number of cities in the core net
NC = 5
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 11111, C = 7014

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 3280 | is_connectable
  164 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  205 | is_connected_step
 6724 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 1200.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 1200.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 7014 rows, 11111 columns and 35301 nonzeros
Model fingerprint: 0xd15d5f42
Model has 5 SOS constraints
Model has 820 general constraints
Variable types: 2460 continuous, 8651 integer (8651 binary)
Coefficient statistics:
  Matrix range     [1e+00, 2e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [7e-01, 4e+01]
Presolve removed 94 rows and 3499 columns
Presolve time: 0.11s
Presolved: 6920 rows, 7612 columns, 35300 nonzeros
Variable types: 2460 continuous, 5152 integer (5152 binary)
Found heuristic solution: objective 87597.887422

Root relaxation: objective 1.914394e+03, 4937 iterations, 0.16 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 1914.39374    0  182 87597.8874 1914.39374  97.8%     -    0s
H    0     0                    56805.036601 1914.39374  96.6%     -    0s
     0     0 3564.07992    0  305 56805.0366 3564.07992  93.7%     -    0s
H    0     0                    50926.655312 3564.07992  93.0%     -    0s
     0     0 3738.75475    0  296 50926.6553 3738.75475  92.7%     -    0s
     0     0 3740.28469    0  299 50926.6553 3740.28469  92.7%     -    0s
     0     0 3740.90141    0  298 50926.6553 3740.90141  92.7%     -    0s
     0     0 3899.40012    0  345 50926.6553 3899.40012  92.3%     -    1s
H    0     0                    26539.674188 3899.40012  85.3%     -    1s
     0     0 3911.88606    0  312 26539.6742 3911.88606  85.3%     -    1s
     0     0 3915.19054    0  339 26539.6742 3915.19054  85.2%     -    1s
     0     0 3916.34523    0  340 26539.6742 3916.34523  85.2%     -    1s
     0     0 3920.04660    0  414 26539.6742 3920.04660  85.2%     -    1s
H    0     0                    23669.699896 3920.04660  83.4%     -    1s
     0     0 3922.05400    0  396 23669.6999 3922.05400  83.4%     -    1s
     0     0 3924.22533    0  374 23669.6999 3924.22533  83.4%     -    1s
     0     0 3924.23491    0  370 23669.6999 3924.23491  83.4%     -    1s
     0     0 3925.43179    0  380 23669.6999 3925.43179  83.4%     -    1s
     0     0 3931.47520    0  325 23669.6999 3931.47520  83.4%     -    1s
     0     0 3933.50681    0  321 23669.6999 3933.50681  83.4%     -    1s
     0     0 3933.68114    0  321 23669.6999 3933.68114  83.4%     -    1s
     0     0 3934.67706    0  383 23669.6999 3934.67706  83.4%     -    1s
     0     0 3934.95569    0  432 23669.6999 3934.95569  83.4%     -    2s
     0     0 3938.19343    0  379 23669.6999 3938.19343  83.4%     -    2s
     0     0 3938.76039    0  355 23669.6999 3938.76039  83.4%     -    2s
     0     0 3939.45811    0  401 23669.6999 3939.45811  83.4%     -    2s
     0     0 3939.45811    0  285 23669.6999 3939.45811  83.4%     -    2s
H    0     0                    22469.259190 3939.45811  82.5%     -    2s
H    0     0                    21416.975854 3939.45811  81.6%     -    2s
     0     2 3939.45811    0  256 21416.9759 3939.45811  81.6%     -    3s
H   36    40                    19642.722575 4175.68588  78.7%   587    3s
H   69    78                    18893.254519 4175.68588  77.9%   383    4s
H   70    78                    17679.651521 4175.68588  76.4%   382    4s
H  107   114                    17018.365748 4175.68588  75.5%   292    4s
H  109   114                    16988.415738 4175.68588  75.4%   289    4s
H  115   122                    16978.415738 4175.68588  75.4%   278    4s
H  116   122                    16301.368904 4175.68588  74.4%   278    4s
   123   146 5670.84482   12  236 16301.3689 4175.68588  74.4%   274    5s
H  586   555                    16299.353706 4175.68588  74.4%   132    7s
H  587   555                    16209.316100 4175.68588  74.2%   132    7s
H  588   555                    16207.300902 4175.68588  74.2%   132    7s
H 1532  1426                    16180.030188 4310.06216  73.4%  97.1    9s
  1719  1521 14045.4724  130  285 16180.0302 4310.06216  73.4%  96.5   13s
  1722  1523 13353.7421  160  230 16180.0302 4310.06216  73.4%  96.4   15s
  1738  1534 15823.9023  148  347 16180.0302 4310.06216  73.4%  95.5   21s
  1751  1542 9361.82547   36  446 16180.0302 4310.06216  73.4%  94.8   25s
  1764  1551 9654.92926   42  462 16180.0302 4310.06216  73.4%  94.1   30s
  1769  1557 4310.06216   12  428 16180.0302 4310.06216  73.4%   139   38s
  1771  1561 4310.06216   13  420 16180.0302 4310.06216  73.4%   141   44s
  1775  1565 4342.70432   14  423 16180.0302 4342.70432  73.2%   147   46s
  1807  1578 8091.42667   16  195 16180.0302 4398.08478  72.8%   173   50s
  1872  1603 7130.48988   19  237 16180.0302 4467.00265  72.4%   202   55s
  1961  1610 4834.29589   23  378 16180.0302 4467.00265  72.4%   227   60s
  2057  1624 5154.51905   26  342 16180.0302 4467.00265  72.4%   253   65s
H 2096  1543                    16180.030180 4467.00265  72.4%   260   66s
  2136  1546 5734.75178   28  394 16180.0302 4467.00265  72.4%   268   71s
H 2140  1470                    16180.030174 4467.00265  72.4%   273   71s
H 2184  1427                    16180.030148 4467.00265  72.4%   276   73s
H 2188  1358                    16180.030120 4467.00265  72.4%   277   73s
H 2202  1289                    16180.030052 4467.00265  72.4%   280   73s
H 2245  1276                    16180.030026 4467.00265  72.4%   284   75s
H 2343  1271                    16180.029930 4467.00265  72.4%   288   77s
H 2404  1202                    16180.029897 4467.00265  72.4%   290   79s
  2426  1206 7361.04525   33  331 16180.0299 4467.00265  72.4%   298   80s
  2772  1255 8837.41302   41  231 16180.0299 4467.00265  72.4%   307   85s
  3049  1331     cutoff   73      16180.0299 4467.00265  72.4%   320   90s
H 3134  1250                    16180.029884 4467.00265  72.4%   319   90s
H 3138  1200                    16180.029856 4467.00265  72.4%   320   90s
  3303  1247 5061.77222   21  369 16180.0299 4533.09817  72.0%   325   95s
  3405  1272 5807.85754   25  348 16180.0299 4533.09817  72.0%   332  101s
  3646  1385 8388.74548   39  176 16180.0299 4882.28951  69.8%   341  106s
  3814  1437 5958.53521   22  331 16180.0299 4882.28951  69.8%   344  110s
  4114  1546 6367.13615   21  378 16180.0299 4887.64188  69.8%   357  116s
  4392  1649 10982.2226   46  138 16180.0299 5154.19900  68.1%   360  121s
  4676  1783 5491.73445   25  298 16180.0299 5342.34938  67.0%   368  125s
  4951  1937 6495.62099   26  322 16180.0299 5349.38269  66.9%   376  152s
  5333  2194 7185.85857   31  243 16180.0299 5900.11012  63.5%   377  157s
  5586  2383 8972.78606   32  217 16180.0299 6012.24571  62.8%   381  162s
  6022  2563 14666.9247   43  121 16180.0299 6072.91145  62.5%   384  167s
  6182  2731 7030.13014   30  268 16180.0299 6158.48294  61.9%   384  170s
  6641  3024 10483.5633   37  311 16180.0299 6235.65100  61.5%   385  177s
  6912  3170 6341.02708   25  365 16180.0299 6283.72715  61.2%   382  181s
  7155  3313 13794.3646   37  195 16180.0299 6371.87181  60.6%   381  185s
H 7380  3453                    16180.029844 6371.87181  60.6%   383  189s
  7616  3611 10771.3420   56  221 16180.0298 6545.70574  59.5%   383  195s
H 7963  3798                    16180.029815 6623.83742  59.1%   384  199s
H 8121  3798                    16180.029807 6654.38122  58.9%   383  199s
  8199  3917 10425.3341   22  357 16180.0298 6708.09270  58.5%   383  203s
H 8204  3917                    16180.029796 6708.09270  58.5%   383  203s
H 8219  3917                    16180.029783 6708.09270  58.5%   384  203s
  8434  4033 14295.6163   35  238 16180.0298 6720.38066  58.5%   387  207s
  8738  4160 15742.3059   32  183 16180.0298 6784.30110  58.1%   389  212s
  9021  4239 11903.3467   41  185 16180.0298 6849.18117  57.7%   391  216s
  9153  4488 11329.2767   36  161 16180.0298 6856.58635  57.6%   391  222s
  9517  4701 8558.07112   25  295 16180.0298 6891.58934  57.4%   389  227s
  9909  4791     cutoff   34      16180.0298 6956.99896  57.0%   389  233s
 10532  4945     cutoff   54      16180.0298 6976.92307  56.9%   382  238s
 10856  5258 7963.80784   32  291 16180.0298 7018.50175  56.6%   382  243s
 11353  5575 11524.3866   60  173 16180.0298 7084.31125  56.2%   379  249s
 11868  5852 12210.3418   44  127 16180.0298 7127.08972  56.0%   376  255s
 12339  6067 7596.54551   36  293 16180.0298 7150.00356  55.8%   376  262s
 12818  6322     cutoff   47      16180.0298 7150.00356  55.8%   376  269s
 13271  6736 9049.08105   67  235 16180.0298 7288.96635  55.0%   376  275s
 13980  6979 14897.4166   34  231 16180.0298 7368.90711  54.5%   372  282s
 14491  7126 10969.6837   32  204 16180.0298 7406.45107  54.2%   374  292s
 14822  7729 9254.78630   34  224 16180.0298 7444.59666  54.0%   372  304s
 16107  7931 9417.71949   39  178 16180.0298 7507.93174  53.6%   366  311s
 16589  8231     cutoff   54      16180.0298 7507.93174  53.6%   369  319s
 17229  8701 infeasible   94      16180.0298 7593.83589  53.1%   366  333s
 17944  8936 12992.8070   42  326 16180.0298 7697.92423  52.4%   364  341s
 18623  9329 10963.8698   36  221 16180.0298 7734.03239  52.2%   364  351s
 19312  9775 9750.96988   36  210 16180.0298 7780.08524  51.9%   363  360s
 20119 10201 10347.7296   42  269 16180.0298 7839.89727  51.5%   362  370s
 21028 10548 11009.3334   27  222 16180.0298 7907.25302  51.1%   360  380s
 21582 10926 9895.42805   41  197 16180.0298 7939.12974  50.9%   358  393s
 22271 10992 9319.31872   36  305 16180.0298 7964.98264  50.8%   358  408s
 22415 11450 10096.8946   52  300 16180.0298 7975.56035  50.7%   359  418s
 23365 11989     cutoff   43      16180.0298 8038.17272  50.3%   357  427s
 24238 12429 9003.74184   31  243 16180.0298 8071.68675  50.1%   355  436s
 25126 12842 12217.9209   35  197 16180.0298 8145.66131  49.7%   354  445s
 26014 13238 9779.26098   71  272 16180.0298 8206.15917  49.3%   354  455s
 26784 13696     cutoff   42      16180.0298 8247.66895  49.0%   355  464s
 27635 14134 16133.8587  100  123 16180.0298 8276.96584  48.8%   354  474s
 28369 14478 14978.9876   61   73 16180.0298 8307.35700  48.7%   355  483s
 29011 14479 13231.4327   42  285 16180.0298 8357.72197  48.3%   354  604s
 29013 14480 12965.6669  106  183 16180.0298 8357.72197  48.3%   354  605s
 29018 14484 14099.3804   58  524 16180.0298 8357.72197  48.3%   354  610s
 29024 14488 12054.2543   55  581 16180.0298 8357.72197  48.3%   354  615s
 29027 14490 14833.4042   30  586 16180.0298 8357.72197  48.3%   354  620s
 29030 14492 11451.9115   36  622 16180.0298 8357.72197  48.3%   354  626s
 29033 14494 15288.4967   53  570 16180.0298 8357.72197  48.3%   354  630s
 29036 14496 8585.22998   33  625 16180.0298 8357.72197  48.3%   354  635s
 29040 14498 15292.7095   71  624 16180.0298 8357.72197  48.3%   354  641s
 29042 14500 10348.0658   36  660 16180.0298 8357.72197  48.3%   353  645s
 29046 14502 14053.3037  102  601 16180.0298 8357.72197  48.3%   353  652s
 29048 14504 12491.6328   53  569 16180.0298 8357.72197  48.3%   353  655s
 29051 14506 10246.9725   45  595 16180.0298 8357.72197  48.3%   353  661s
 29053 14507 12242.6917   38  588 16180.0298 8357.72197  48.3%   353  665s
 29056 14509 14163.1419   62  618 16180.0298 8357.72197  48.3%   353  672s
 29058 14510 10336.3848   30  603 16180.0298 8357.72197  48.3%   353  676s
 29060 14512 9674.33490   41  628 16180.0298 8357.72197  48.3%   353  680s
H29060 13785                    16180.029757 8357.72197  48.3%   353  683s
 29062 13786 11220.3153   62  677 16180.0298 8357.72197  48.3%   353  686s
 29064 13787 13066.6936   37  661 16180.0298 8357.72197  48.3%   353  691s
 29066 13789 13358.5927   34  709 16180.0298 8357.72197  48.3%   353  695s
 29069 13791 10148.5974   30  616 16180.0298 8357.72197  48.3%   353  700s
 29072 13793 14196.3203   66  605 16180.0298 8357.72197  48.3%   353  706s
 29075 13795 13514.4342   67  583 16180.0298 8357.72197  48.3%   353  711s
 29077 13796 9206.10047   48  678 16180.0298 8357.72197  48.3%   353  715s
 29080 13798 9060.04799   28  609 16180.0298 8357.72197  48.3%   353  721s
 29082 13799 15307.2589   39  630 16180.0298 8357.72197  48.3%   353  725s
 29085 13801 10998.3605   40  620 16180.0298 8357.72197  48.3%   353  731s
 29088 13803 15032.9321   47  703 16180.0298 8357.72197  48.3%   353  737s
 29090 13805 12268.2066   49  660 16180.0298 8357.72197  48.3%   353  741s
 29093 13807 15220.8756   35  681 16180.0298 8357.72197  48.3%   353  746s
 29096 13809 9783.68590   41  725 16180.0298 8357.72197  48.3%   353  751s
 29098 13810 9525.09566   36  630 16180.0298 8357.72197  48.3%   353  755s
 29101 13812 9703.83391   40  673 16180.0298 8357.72197  48.3%   353  761s
 29104 13814 12621.0780   43  735 16180.0298 8357.72197  48.3%   353  766s
 29106 13815 11004.9843   41  654 16180.0298 8357.72197  48.3%   353  770s
 29109 13817 9954.46313   30  653 16180.0298 8357.72197  48.3%   353  775s
 29112 13819 10154.2110   45  645 16180.0298 8357.72197  48.3%   353  781s
 29114 13821 12932.8017   45  622 16180.0298 8357.72197  48.3%   353  785s
 29117 13823 10625.4411   26  631 16180.0298 8357.72197  48.3%   353  790s
 29120 13825 12686.8933   57  666 16180.0298 8357.72197  48.3%   353  795s
 29123 13827 12226.4637   33  728 16180.0298 8357.72197  48.3%   352  800s
 29126 13829 15514.4880   60  715 16180.0298 8357.72197  48.3%   352  806s
 29128 13830 9500.22897   40  686 16180.0298 8357.72197  48.3%   352  810s
 29131 13832 12992.1294   43  689 16180.0298 8357.72197  48.3%   352  815s
 29134 13834 13125.5418   55  712 16180.0298 8357.72197  48.3%   352  821s
 29136 13835 8585.22998   33  734 16180.0298 8357.72197  48.3%   352  825s
 29139 13837 14031.7203   46  689 16180.0298 8357.72197  48.3%   352  831s
 29141 13839 14588.8132   68  716 16180.0298 8357.72197  48.3%   352  836s
 29143 13840 12171.8122   40  630 16180.0298 8357.72197  48.3%   352  840s
 29146 13842 14053.3037  102  656 16180.0298 8357.72197  48.3%   352  846s
 29148 13843 12491.6328   53  647 16180.0298 8357.72197  48.3%   352  850s
 29151 13845 10246.9725   45  729 16180.0298 8357.72197  48.3%   352  855s
 29154 13847 8723.07661   36  689 16180.0298 8357.72197  48.3%   352  861s
 29156 13849 14163.1419   62  683 16180.0298 8357.72197  48.3%   352  865s
 29159 13851 12501.0246   54  756 16180.0298 8357.72197  48.3%   352  871s
 29161 13852 14147.9098   53  704 16180.0298 8357.72197  48.3%   352  875s
 29163 13853 12407.0379   40  663 16180.0298 8357.72197  48.3%   352  880s
 29166 13855 13358.5927   34  693 16180.0298 8357.72197  48.3%   352  885s
 29169 13857 10148.5974   30  721 16180.0298 8357.72197  48.3%   352  890s
 29172 13859 14196.3203   66  627 16180.0298 8357.72197  48.3%   352  896s
 29175 13861 13514.4342   67  698 16180.0298 8357.72197  48.3%   352  901s
 29177 13863 9206.10047   48  697 16180.0298 8357.72197  48.3%   352  905s
 29180 13865 9060.04799   28  717 16180.0298 8357.72197  48.3%   352  911s
 29183 13867 8822.04297   45  751 16180.0298 8357.72197  48.3%   352  916s
 29185 13868 10998.3605   40  735 16180.0298 8357.72197  48.3%   352  921s
 29187 13869 9830.16764   34  692 16180.0298 8357.72197  48.3%   352  925s
 29190 13871 12268.2066   49  690 16180.0298 8357.72197  48.3%   352  931s
 29193 13873 15220.8756   35  685 16180.0298 8357.72197  48.3%   352  936s
 29196 13875 9783.68590   41  654 16180.0298 8357.72197  48.3%   352  941s
 29198 13877 9525.09566   36  733 16180.0298 8357.72197  48.3%   352  945s
 29201 13879 9703.83391   40  775 16180.0298 8357.72197  48.3%   352  950s
 29203 13880 10068.9848   35  761 16180.0298 8395.43137  48.1%   352  955s
 29206 13882 11004.9843   41  749 16180.0298 8454.54433  47.7%   351  961s
 29208 13883 10297.2052   34  737 16180.0298 8468.25455  47.7%   351  965s
 29211 13885 13231.4327   42  697 16180.0298 8488.68002  47.5%   351  971s
 29213 13887 12965.6669  106  704 16180.0298 8507.16995  47.4%   351  975s
 29216 13889 10040.8100   33  770 16180.0298 8565.15809  47.1%   351  980s
 29219 13891 13063.8994   79  760 16180.0298 8613.84401  46.8%   351  985s
 29222 13893 15431.4637   46  808 16180.0298 8651.76495  46.5%   351  991s
 29224 13894 12054.2543   55  787 16180.0298 8661.16914  46.5%   351  995s
 29227 13896 14833.4042   30  760 16180.0298 8682.43956  46.3%   351 1000s
 29230 13898 11451.9115   36  721 16180.0298 8687.76817  46.3%   351 1009s
 29231 13899 12992.1294   43  721 16180.0298 8687.76817  46.3%   351 1015s
 29232 13902 8687.76817   22  655 16180.0298 8687.76817  46.3%   366 1036s
 29234 13906 8687.76817   23  727 16180.0298 8687.76817  46.3%   366 1059s
 29238 13910 8687.76817   24  687 16180.0298 8687.76817  46.3%   367 1077s
 29246 13913 8687.76817   25  675 16180.0298 8687.76817  46.3%   368 1094s
 29254 13916 8687.76817   26  634 16180.0298 8687.76817  46.3%   369 1108s
 29262 13915 8687.76817   26  377 16180.0298 8687.76817  46.3%   370 1116s
 29270 13920 8687.76817   27  643 16180.0298 8687.76817  46.3%   370 1121s
 29278 13921 8687.76817   27  536 16180.0298 8687.76817  46.3%   371 1129s
 29287 13919 8687.76817   28  574 16180.0298 8687.76817  46.3%   371 1133s
 29297 13919 9444.40889   28  599 16180.0298 8687.76817  46.3%   372 1141s
 29310 13921 8687.76817   29  606 16180.0298 8687.76817  46.3%   373 1147s
 29323 13919     cutoff   29      16180.0298 8687.76817  46.3%   373 1150s
 29358 13910 8687.76817   30  507 16180.0298 8687.76817  46.3%   375 1158s
 29373 13916 8687.76817   31  614 16180.0298 8687.76817  46.3%   376 1164s
 29389 13914 8687.76817   32  558 16180.0298 8687.76817  46.3%   376 1169s
 29404 13913 8687.76817   32  522 16180.0298 8687.76817  46.3%   377 1173s
 29425 13915 8687.76817   33  485 16180.0298 8687.76817  46.3%   378 1179s
 29448 13913 8687.76817   34  490 16180.0298 8687.76817  46.3%   379 1183s
 29471 13913 8687.76817   35  503 16180.0298 8687.76817  46.3%   380 1186s
 29500 13908 8687.76817   35  466 16180.0298 8687.76817  46.3%   381 1190s
 29536 13902 8687.76817   36  486 16180.0298 8687.76817  46.3%   382 1195s
 29566 13899 8687.76817   36  445 16180.0298 8687.76817  46.3%   382 1200s

Cutting planes:
  Gomory: 78
  Cover: 315
  Clique: 2
  MIR: 216
  Flow cover: 987
  GUB cover: 247
  Zero half: 44
  RLT: 494
  Relax-and-lift: 15

Explored 29595 nodes (11353717 simplex iterations) in 1200.02 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 16180 16180 16180 ... 16209.3

Time limit reached
Best objective 1.618002975670e+04, best bound 8.687768166107e+03, gap 46.3056%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	4.5	4.000000e+00	8.500000
1	1	Borås	False	False	0.7	6.400000e+00	7.100000
2	2	Eskilstuna	True	False	1.1	1.113000e+02	112.400000
3	3	Falun	False	False	2.3	2.167155e-13	2.300000
4	4	Gävle	True	False	1.0	3.950000e+01	40.500000
5	5	Göteborg	False	False	5.8	4.165557e-13	5.800000
6	6	Halmstad	False	False	3.4	2.200000e+00	5.600000
7	7	Haparanda	False	False	4.6	8.881784e-15	4.600000
8	8	Helsingborg	False	False	2.2	0.000000e+00	2.200000
9	9	Hudiksvall	False	False	3.9	2.740000e+01	31.300000
10	10	Jönköping	False	False	1.2	4.192202e-13	1.200000
11	11	Kalmar	False	False	4.0	1.600000e+00	5.600000
12	12	Karlskrona	False	False	1.6	5.329071e-13	1.600000
13	13	Karlstad	False	False	0.9	4.050094e-13	0.900000
14	14	Kiruna	False	False	4.0	1.563194e-13	4.000000
15	15	Kristianstad	False	False	3.0	3.899999e+00	6.899999
16	16	Lidköping	False	False	1.2	1.340000e+01	14.600000
17	17	Linköping	False	False	4.1	1.250000e+01	16.600000
18	18	Luleå	False	False	2.0	1.310000e+01	15.100000
19	19	Malmö	False	False	2.6	1.090683e-12	2.599999
20	20	Motala	False	False	2.4	1.691092e-12	2.400000
21	21	Norrköping	False	False	1.7	2.400000e+01	25.700000
22	22	Nyköping	False	False	4.6	1.129763e-12	4.600000
23	23	Sandviken	True	False	4.5	3.700000e+00	8.200000
24	24	Skellefteå	False	False	4.4	1.510000e+01	19.500000
25	25	Skövde	False	False	2.5	7.100000e+00	9.600000
26	26	Stockholm	False	False	7.0	1.307399e-12	7.000000
27	27	Sundsvall	False	False	0.7	2.670000e+01	27.400000
28	28	Trelleborg	False	False	1.3	1.151079e-12	1.300000
29	29	Uddevalla	False	False	4.0	5.800000e+00	9.800000
30	30	Umeå	False	False	3.2	1.950000e+01	22.700000
31	31	Uppsala	True	False	1.9	4.750000e+01	49.400000
32	32	Varberg	False	False	0.8	5.600000e+00	6.400000
33	33	Vetlanda	False	False	2.1	9.199999e+00	11.299999
34	34	Vänersborg	False	False	3.6	9.800000e+00	13.400000
35	35	Västervik	False	False	1.8	5.600000e+00	7.400000
36	36	Västerås	True	True	1.4	1.124000e+02	1.400000
37	37	Växjö	False	False	2.3	6.899999e+00	9.199999
38	38	Örebro	False	False	4.1	2.750000e+01	31.599999
39	39	Örnsköldsvik	False	False	3.1	2.270000e+01	25.800000
40	40	Östersund	False	False	0.9	1.200817e-12	0.900000

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	19.500000	731.139140	111
1	Eskilstuna	Västerås	CORE	112.400000	430.000000	43
2	Gävle	Hudiksvall	SUB	-31.300000	1278.724564	118
3	Skövde	Örebro	SUB	9.600000	470.396598	132
4	Lidköping	Örebro	SUB	14.600000	841.276327	148
5	Linköping	Norrköping	SUB	16.600000	163.209997	44
6	Motala	Örebro	SUB	2.400000	76.971956	92
7	Boden	Kiruna	SUB	-4.000000	637.912915	291
8	Linköping	Vetlanda	SUB	-11.299999	589.291362	138
9	Sundsvall	Östersund	SUB	-0.900000	70.870188	166
10	Kalmar	Västervik	SUB	5.600000	300.208566	139
11	Halmstad	Helsingborg	SUB	-2.200000	58.849848	79
12	Umeå	Örnsköldsvik	SUB	22.700000	849.479925	111
13	Boden	Luleå	SUB	8.500000	58.656836	32
14	Eskilstuna	Örebro	SUB	-31.599999	765.621338	83
15	Karlstad	Örebro	SUB	0.900000	29.229970	77
16	Halmstad	Varberg	SUB	5.600000	102.802146	65
17	Lidköping	Vänersborg	SUB	-13.400000	206.938967	60
18	Uddevalla	Vänersborg	SUB	9.800000	39.823252	21
19	Gävle	Sandviken	CORE	-8.200000	250.000000	25
20	Sandviken	Västerås	CORE	-1.400000	1100.000000	110
21	Jönköping	Linköping	SUB	1.200000	63.032985	125
22	Sundsvall	Örnsköldsvik	SUB	-25.800000	1177.683863	127
23	Eskilstuna	Nyköping	SUB	-4.600000	119.995513	83
24	Kristianstad	Malmö	SUB	-2.599999	84.931606	94
25	Borås	Varberg	SUB	-6.400000	165.811270	84
26	Vetlanda	Växjö	SUB	-9.199999	187.740561	72
27	Eskilstuna	Uppsala	CORE	-49.400000	1050.000000	105
28	Kristianstad	Trelleborg	SUB	-1.300000	53.600690	104
29	Haparanda	Luleå	SUB	4.600000	210.858567	124
30	Luleå	Skellefteå	SUB	15.100000	742.410216	133
31	Stockholm	Uppsala	SUB	7.000000	136.873721	69
32	Kristianstad	Växjö	SUB	6.899999	296.827592	120
33	Norrköping	Västervik	SUB	-7.400000	287.369532	112
34	Falun	Sandviken	SUB	2.300000	48.115171	65
35	Gävle	Uppsala	CORE	40.500000	600.000000	60
36	Kalmar	Karlskrona	SUB	-1.600000	45.527170	79
37	Göteborg	Uddevalla	SUB	5.800000	117.417493	70
38	Hudiksvall	Sundsvall	SUB	-27.400000	641.652301	81
39	Eskilstuna	Norrköping	SUB	-25.700000	847.208022	102
40	Borås	Skövde	SUB	7.100000	251.569591	105

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  3430.000
subnet = 12750.030
------------------
total  = 16180.030

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))